Operational System Analysis on Iron Ore Exportation in Rio de Janeiro

Transcription

1 EngOpt 28 - International Conference on Engineering Optimization Rio de Janeiro, Brazil, 1-5 June 28. Operational System Analysis on Iron Ore Exportation in Rio de Janeiro João Carlos Dias Barroso Josiane Corrêa de Oliveira Mauro Rezende Filho Rodrigo Pires Dória Sueli Cunha BARROSO: Rio de Janeiro, Brazil, joao.barroso@vale.com OLIVEIRA: Rio de Janeiro, Brazil, josianecorrea@yahoo.com.br REZENDE FILHO: Rio de Janeiro, Brazil, rezende_m@yahoo.com.br DÓRIA: Rio de Janeiro, Brazil, rodrigo.doria@superig.com.br CUNHA: Rio de Janeiro, Brazil, suelicunha@ugf.br Abstract The iron ore market is a highly lucrative business within world s scenario. Vale Corporation has a strong influence in this market, exporting around 2 millions iron ore tons per year. The company invests millions in infrastructure, technology and operations optimization that result on a large scale production. This paper shows the shipment operations practiced in one unit of this company in Brazil and a study about it. The process of iron ore shipment is strategic for Vale, and it involves several restrictions within navigation area that presents a direct impact on company s results. This paper focus on decision processes analysis between two strategies: the first one, to continue with the current operational shipment strategy; the second one, to adopt another already used which presents some navigability restrictions and could affect the company operational and financial results. Keywords: Iron ore, Simulation, Arena, Data analysis. 1. Introduction The objective of this paper is to study two iron ore shipment strategy and understand which one is the better and most lucrative way for the company involved. This paper is based on simulation techniques used to reproduce Vale iron ore exportation in one unit of this company. The following sections presents a summary about the company, some shipment and port concepts (needed to understand some simulation steps) and the case study, basis of this paper, as well as its conclusions. Tools, techniques, data used and results obtained will also be presented in the next pages. 2. Company presentation Vale is a pioneering mining company that works diligently to transform mineral resources. Vale s aim is to continually improve the company and to exceed current standards of excellence in the extraction and production of minerals. Vale is a global company headquartered in Brazil, with a workforce of over 1, employees, including outsourced workers. Vale operates on five continents (Figure 1) through its mining operations, mineral research plants, and commercial offices. Figure 1. Vale operations placement The company was created by the Brazilian government in 1942 under the name Companhia Vale do Rio Doce (CVRD). In 1997, Vale became a privately run company. Vale is now a global company, that works to transform mineral resources into sustainable development [1]. The unit studied on this paper is a shipping port facilities, located in Itaguaí (RJ), the Sepetiba Port. It is operated by 168 Sepetiba Bay Port Company (CPBS, Companhia Portuária Baía de Sepetiba) employees, apart from circa 2 contractors. Figure 2 shows its location using part of Brazil s map and Figure 3 presents the map of Sepetiba Port. CVRD s ore is transported in Fábrica s and Corrego do Feijão s mines up to the Port via the MRS Logistics railway. It has four yards, with a total capacity of 1.2 million tons

2 of ore. It also counts on a dock berth with an embarking capacity of 15 million tons of ore annually. The Sepetiba ore is managed by CVRD s Ore management board as of September 23, through the Sepetiba Bay Port Company. The change was due to the merger with Ferteco by CVRD. Figure 2. Sepetiba Port location Figure 3. Sepetiba Port map 3. Draft mark and shipment concepts For a better understand of each strategy analyzed in this paper, follow a section dedicated to explain part of the knowledge needed about draft mark and shipment concepts. The draft (or draught) of a ship's hull is the vertical distance between the waterline and the bottom of the hull (keel), with the thickness of the hull included [2]. In the case of not being included the draft outline would be obtained. Draft determines the minimum depth of water a ship or boat can safely navigate. The draft aft (stern) is measured in the perpendicular of the stern. The draft forward (bow (ship)) is measured in the perpendicular of the bow. The mean draft is obtained by calculating from the averaging of the stern and bow drafts, with some corrections (water level variation and others). Figure 4 shows an example of draft mark. Figure 4. Draft mark example 3.1. Variations of the draft The draft of a ship can be affected by multiple factors, not considering the rise and fall of the ship by displacement: draft variation by list, draft variation by water level change, allowance of fresh water draft variation by passage from fresh to sea water or vice versa, heat variation in navigating shallow waters.

3 3.2. The draft scale The drafts are measured with a banded scale, from bow and to stern, and for some ships, the average perpendicular measurement is also used. Two types of scales are used: one is measured in decimeters, with only even numbers represented, other measured in feet. Both odd and even numbers are represented and usually marked in roman numerals. The interpretation of the draft scale is achieved by the following: the number in feet indicates the draft, the height of the number being a decimeter or 1/2 a foot (6 inches), according to the particular case. Therefore, the positions are obtained in proportion. In order to relate both scales, the equivalent measurements in feet, inches and centimeters are indicated (1 foot = 12 inches, 1 inch = 2.54 cm, 1 foot = 3.48 cm). Figures 5 and 6 show examples of draft scale. Figure 5. Metric bow scale Figure 6. English system in Roman numeration of the bow scale Larger ships try to maintain an average water draft when they are light (without cargo), in order to make a better sea crossing and reduce the effects of the wind (high centre of velic force). To do this, they use sailing ballasts to stabilize the ship, following the unloading of cargo. The water draft of a large ship has little direct link with its stability, the latter depends solely on the respective positions of the metacenter of the hull and the centre of gravity. It is also true however, that a light ship has quite high stability which can lead to implying too much rolling of the ship (due to memory). A fully loaded ship (with a large draft) can have a strong or on the contrary, a weak stability depends upon the manner by which the ship is loaded (height of the centre of gravity). The draft of ships can be increased when the ship is in motion, a phenomenon known as squat (shipping term, nautical term for the action of the stern settling deeper when power is applied). Now that the knowledge about draft and shipment was presented, follow the case study about operational system analysis on iron ore exportation at Vale s Sepetiba Port. 4. Case study After a brief description of the company and the learn of some draft and shipment concepts, the case study begins presenting the two strategies that will be explained and simulated using specific software for this kind of analysis. Then, the software used will be presented, as well as its templates and all data used to analyze the case. Finally, all results will also be presented and compared with real operations. Vale has already used both strategies described in the following sections (4.1 and 4.2). At Sepetiba Port, is allowed a draft until 18.1m Strategy 1 All draft marks Depending on weather conditions every large vessel can stay at the port and be used for iron ore exportation. High level of iron ore charge can be used. When a charge is load into the ship with a draft above than 17.1m is necessary to wait the water level go up so as the ship will be able to leave Sepetiba Port. Also, sometimes is necessary to wait the sunlight to allow a ship to leave the port. Using this strategy, Vale will load high level of iron ore volume, but it will create a long ship queue waiting for the processes of charge. Should be remembered that after an specific time, some taxes can be part of the processes. For example, the demurrage (an ancillary cost that represents liquidated damages for delays), occurs when the vessel is prevented from the loading or discharging of cargo within the stipulated laytime Strategy 2 Using a draft until 17.1m This strategy is similar to the one presented above, every larger vessel can transport iron ore. But there is a unique restriction on it. All vessels will load the product until a draft mark of 17.1m. It means that after the load process the vessel will be ready to leave the port allowing another ship to start the charge processes.

4 5. Simulation The tool utilized for simulation development and analysis was the software Arena 9.. Arena is a software of processes simulation where users can define, throughout some templates, each process and their respectively properties based on specific needs [3]. All steps from real processes should be defined with entities, resources, availability, time, occurrences, statistics distribution involved and several other variables that should be considered. The objective is to simulate the real world with a software, trying to optimize (virtually) some production steps and analyze the results to understand better ways of performance. Also, it is a cheaper way to study any company processes instead of implement this to evaluate the possible gains within real operations. Simulations still give a better and detailed understand of each small process and how it affects the whole company or production Templates used and simulation details The software Arena 9. has some templates that were utilized to simulate CPBS iron ore shipment process [4]. Figures 7 and 8 illustrate the Arena templates structure of each CPBS processes strategy. First of all, is represented the vessel arrive tax in the port. All statistics distribution will be presented in the Section 5.2. Then, is defined the allocation of the resource Port, that only permit one ship per time during the shipment processes. Next, is described the starting measure of vessel charger time processes followed by the definition of the real process of iron ore load into the vessel. The registration of all data needed to analyze results is then represented. Only in the Strategy 1, a decision is made according on the draft mark a specific ship has, since in the Strategy 2 only a draft mark of 17.1 is allowable. Then, all time spent on each process is expressed, like a load process or a process of waiting some weather conditions to leave the port. Now, another vessel is allowed to start the shipment processes, releasing the resource previously allocated. Lastly, a permission to leave the port finalizes an entity participation into the whole process. V essels with draft mark of 17.1 Port_allocation load process measure volume loaded waiting permission to leave leaving port Draft mark above 17.1 load time measure load time identify type of draft E n t i t y. T y p e = = N a v i o s C a l a d o s Number of vessels E l s e permission obtained waiting permission to leave_ Number of vessels_ Figure 7. Strategy 1 Arena templates structure Vessels with Draft of 17.1m load time measure load time waiting permission permission obtained Port_allocation load process measure volume loaded number of vessels leaving port Figure 8. Strategy 2 Arena templates structure 5.2. Data used and statistics distribution involved To develop real processes simulations with a software is necessary to obtain data from real processes. For this case study were utilized data of two years and half of CPBS operations. For subjects of information security, within this paper will only be presented some results comparison and analysis. All outlayers were removed from the data collected to do not affect processes simulations. The data obtained from Vale were the vessels arrive tax, iron ore load process time and waiting time for a ship to leave the port. All data was analyzed with an Arena tool called Input Analyzer. This tool shows the best statistic curve that can be applied for the data obtained. Table 1 shows the period of data obtained, the kind of tax referred, the draft mark for each value and finally, the statistics distribution obtained from input analyze and how it was inserted into Arena s templates.

5 Table 1. All data used within Arena s simulation with it respectively statistics distribution Period Kind of tax Draft Mark Distribution Expression utilized from Jan25 to Jun26 Vessels arrive 17.1m Gamma 2 + GAMM(21,.986) from Jan25 to Iron ore load 17.1m Beta * BETA(1.1, 2.23) Jun26 from Jan25 to Jun26 from Jan25 to Jun26 from Jan25 to Jun26 from Jan25 to Jun26 from Jul26 to Sep27 from Jul26 to Sep27 from Jul26 to Set27 Waiting to leave port 17.1m LogNormal LOGN(3.3, 1.98) Vessels arrive above 17.1m Gamma 1 + GAMM(88,.999) Iron ore load above 17.1m Gamma 26 + GAMM(6.93, 3.26) Waiting to leave port above 17.1m Beta 32 * BETA(.896, 3.19) Vessels arrive 17.1m Weibull WEIB(53.6, 1.4) Iron ore load 17.1m Beta * BETA(4.54, 6.79) Waiting to leave port 17.1m LogNormal LOGN(1.94, 1.1) 6. Simulation results and its analysis The simulation was based on two strategies already presented in this paper and used by Vale. After develop the simulation structure and define statistics involved, the simulation starts and it results should be compared with the real data to validate the model proposed. Once both strategies were already used by the company, is easy to validate the template developed and to try another simulations with the same model. All comparisons made between the simulated results and the real data shown that the proposed model can be validated based on similar results from Arena and Vale obtained values. Table 2 shows all results obtained with simulation processes and the comparison with real data. All values for this paper were modified to preserve information security of all data obtained. Table 2. Simulation results and comparison with real values Strategy 1 - Jan/25 to Jun/26 all draft marks Items Real data Simulated data # Vessels volume # Vessels with draft until 17.1m # Vessels with draft above 17.1m Period 18 months 18 months Iron Ore Volume Shipped (tons) 29,67,923 3,515,652 Strategy 2 - Jul/26 to Sep/27 draft until 17.1m Items Real data Simulated data # Vessels volume # Vessels with draft until 17.1m # Vessels with draft above 17.1m Period 15 months 15 months Iron Ore Volume Shipped (tons) 26,412,666 27,484,863 Model Simulated - Jul/26 to Sep/27 all draft marks Items Real data Simulated data # Vessels volume # Vessels with draft until 17.1m # Vessels with draft above 17.1m 126 Period 15 months 15 months Iron Ore Volume Shipped (tons) 26,412,666 27,542,181 Table 2 presents strategy 1 and 2 simulations and Arena models validation once time the values obtained are very similar and close to the real ones. The model simulated data means what was studied with this paper, one strategy applied to the same period of the other one (with the same statistics distribution) and the comparison between both strategies. As it shows, the model simulated do not present a significant level of iron ore volume increase. To finalize all results analysis for this paper only the simulated data were used. As Table 3 shows, if were made the comparison

6 between the sum of strategy 1 and strategy 2 simulated data with the sum of strategy 1 with the model simulated data can be concluded that when the focus of analysis is iron ore shipped volume, there is no significant difference between values obtained. Although, when the focus of analysis changes for the number of vessels, this table shows that with a lower number of vessels could be shipped a similar volume of iron ore. Table 3. Final results comparison Obtained Values Comparison Strategy1+Strategy2 Strategy1+Model simulated Items Difference (Simulated data only) (Simulated data only) # Vessels volume (49) # Vessels with draft until 17.1m (175) # Vessels with draft above 17.1m Period 33 months 33 months - Iron Ore Volume Shipped (tons) 58,,515 58,57,833 57, Conclusions This paper presented the company (basis of the case studied) with its respectively iron ore exportation processes. Also, a case study of this company was presented and shown two strategies to export iron ore. This paper shown some simulation steps trying to reproduce the real processes used by the company involved. Each strategy was detailed and analyzed step by step and all simulations results were compared. In the end of case study we conclude that both strategies are similar considering the total iron ore volume exported by each one (using simulations data from Arena 9.). The next steps for this kind of study are to obtain more data from the company and try to simulate the whole processes used at CPBS within exportation steps, since logistics railway to the shipment of iron ore by vessels. More variables should be considered and other templates should be developed with the software used. All these steps will take a long time to be studied and probably will highlight some specific issues that Vale should improve and develop more and more. Also, should be considered the time used to fix any broke equipments and the time spent with some port issues. This kind of project shows the high relevance and importance of engineering of optimization and shows that some techniques can be used with several kind of market and it respectively processes. It will always drive companies to a completely knowledge of their processes and understand their issues and improvement plans needed. 8. References 1. VALE. Available at: < accessed on April THE Free Dictionary. Available at: < accessed on April ARAÚJO, Luciane Calixto de; SOUZA, Alisson D. C.; LIMA, Rodrigo Zago de. Manual do Arena 9.. Apostila. Santa Catarina: UFSC, BARROSO, João Carlos Dias; DÓRIA, Rodrigo Pires; OLIVEIRA, Josiane Corrêa de. Análise de metodologia operacional para ganhos em escala na exportação de minério de ferro pelo Porto de Itaguaí RJ. 27. Trabalho de Conclusão de Curso Curso de Engenharia de Produção, Universidade Gama Filho, Rio de Janeiro.